docs: v2 상태 반영 - 전체 문서 현행화 (#131)
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- testing.md: 54 tests/4 files → 551 tests/25 files 반영, 전체 테스트 파일 설명 - architecture.md: v2 컴포넌트 추가 (Strategy, Context, Dashboard, Decision Logger 등), Playbook Mode 데이터 플로우, DB 스키마 5개 테이블, v2 환경변수 - commands.md: Dashboard 실행, Telegram 명령어 9종 레퍼런스 - CLAUDE.md: Project Structure 확장, 테스트 수 업데이트, --dashboard 플래그 - skills.md: DB 파일명 trades.db로 통일, Dashboard 명령어 추가 - requirements-log.md: 2026-02-16 문서 v2 동기화 요구사항 기록 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
22
CLAUDE.md
22
CLAUDE.md
@@ -15,6 +15,9 @@ pytest -v --cov=src
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# Run (paper trading)
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python -m src.main --mode=paper
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# Run with dashboard
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python -m src.main --mode=paper --dashboard
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```
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## Telegram Notifications (Optional)
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@@ -43,6 +46,10 @@ Get real-time alerts for trades, circuit breakers, and system events via Telegra
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- ℹ️ Market open/close notifications
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- 📝 System startup/shutdown status
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### Interactive Commands
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With `TELEGRAM_COMMANDS_ENABLED=true` (default), the bot supports 9 bidirectional commands: `/help`, `/status`, `/positions`, `/report`, `/scenarios`, `/review`, `/dashboard`, `/stop`, `/resume`.
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**Fail-safe**: Notifications never crash the trading system. Missing credentials or API errors are logged but trading continues normally.
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## Smart Volatility Scanner (Optional)
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@@ -109,17 +116,23 @@ User requirements and feedback are tracked in [docs/requirements-log.md](docs/re
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```
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src/
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├── analysis/ # Technical analysis (RSI, volatility, smart scanner)
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├── backup/ # Disaster recovery (scheduler, cloud storage, health)
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├── brain/ # Gemini AI decision engine (prompt optimizer, context selector)
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├── broker/ # KIS API client (domestic + overseas)
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├── brain/ # Gemini AI decision engine
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├── context/ # L1-L7 hierarchical memory system
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├── core/ # Risk manager (READ-ONLY)
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├── evolution/ # Self-improvement optimizer
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├── dashboard/ # FastAPI read-only monitoring (8 API endpoints)
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├── data/ # External data integration (news, market data, calendar)
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├── evolution/ # Self-improvement (optimizer, daily review, scorecard)
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├── logging/ # Decision logger (audit trail)
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├── markets/ # Market schedules and timezone handling
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├── notifications/ # Telegram real-time alerts
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├── notifications/ # Telegram alerts + bidirectional commands (9 commands)
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├── strategy/ # Pre-market planner, scenario engine, playbook store
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├── db.py # SQLite trade logging
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├── main.py # Trading loop orchestrator
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└── config.py # Settings (from .env)
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tests/ # 343 tests across 14 files
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tests/ # 551 tests across 25 files
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docs/ # Extended documentation
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```
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@@ -131,6 +144,7 @@ ruff check src/ tests/ # Lint
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mypy src/ --strict # Type check
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python -m src.main --mode=paper # Paper trading
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python -m src.main --mode=paper --dashboard # With dashboard
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python -m src.main --mode=live # Live trading (⚠️ real money)
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# Gitea workflow (requires tea CLI)
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@@ -2,7 +2,9 @@
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## Overview
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Self-evolving AI trading agent for global stock markets via KIS (Korea Investment & Securities) API. The main loop in `src/main.py` orchestrates four components across multiple markets with two trading modes: daily (batch API calls) or realtime (per-stock decisions).
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Self-evolving AI trading agent for global stock markets via KIS (Korea Investment & Securities) API. The main loop in `src/main.py` orchestrates components across multiple markets with two trading modes: daily (batch API calls) or realtime (per-stock decisions).
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**v2 Proactive Playbook Architecture**: The system uses a "plan once, execute locally" approach. Pre-market, the AI generates a playbook of scenarios (one Gemini API call per market per day). During trading hours, a local scenario engine matches live market data against these pre-computed scenarios — no additional AI calls needed. This dramatically reduces API costs and latency.
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## Trading Modes
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@@ -46,9 +48,11 @@ High-frequency trading with individual stock analysis:
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**KISBroker** (`kis_api.py`) — Async KIS API client for domestic Korean market
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- Automatic OAuth token refresh (valid for 24 hours)
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- Leaky-bucket rate limiter (10 requests per second)
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- Leaky-bucket rate limiter (configurable RPS, default 2.0)
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- POST body hash-key signing for order authentication
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- Custom SSL context with disabled hostname verification for VTS (virtual trading) endpoint due to known certificate mismatch
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- `fetch_market_rankings()` — Fetch volume surge rankings from KIS API
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- `get_daily_prices()` — Fetch OHLCV history for technical analysis
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**OverseasBroker** (`overseas.py`) — KIS overseas stock API wrapper
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@@ -63,10 +67,7 @@ High-frequency trading with individual stock analysis:
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- `is_market_open()` checks weekends, trading hours, lunch breaks
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- `get_open_markets()` returns currently active markets
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- `get_next_market_open()` finds next market to open and when
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**New API Methods** (added in v0.9.0):
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- `fetch_market_rankings()` — Fetch volume surge rankings from KIS API
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- `get_daily_prices()` — Fetch OHLCV history for technical analysis
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- 10 global markets defined (KR, US_NASDAQ, US_NYSE, US_AMEX, JP, HK, CN_SHA, CN_SZA, VN_HNX, VN_HSX)
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### 2. Analysis (`src/analysis/`)
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@@ -91,14 +92,9 @@ High-frequency trading with individual stock analysis:
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- **Fallback**: Uses static watchlist if ranking API unavailable
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- **Realtime mode only**: Daily mode uses batch processing for API efficiency
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**Benefits:**
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- Reduces Gemini API calls from 20-30 stocks to 1-3 qualified candidates
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- Fast Python-based filtering before expensive AI judgment
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- Logs selection context (RSI, volume_ratio, signal, score) for Evolution system
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### 3. Brain (`src/brain/`)
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### 3. Brain (`src/brain/gemini_client.py`)
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**GeminiClient** — AI decision engine powered by Google Gemini
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**GeminiClient** (`gemini_client.py`) — AI decision engine powered by Google Gemini
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- Constructs structured prompts from market data
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- Parses JSON responses into `TradeDecision` objects (`action`, `confidence`, `rationale`)
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@@ -106,11 +102,20 @@ High-frequency trading with individual stock analysis:
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- Falls back to safe HOLD on any parse/API error
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- Handles markdown-wrapped JSON, malformed responses, invalid actions
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**PromptOptimizer** (`prompt_optimizer.py`) — Token efficiency optimization
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- Reduces prompt size while preserving decision quality
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- Caches optimized prompts
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**ContextSelector** (`context_selector.py`) — Relevant context selection for prompts
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- Selects appropriate context layers for current market conditions
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### 4. Risk Manager (`src/core/risk_manager.py`)
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**RiskManager** — Safety circuit breaker and order validation
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⚠️ **READ-ONLY by policy** (see [`docs/agents.md`](./agents.md))
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> **READ-ONLY by policy** (see [`docs/agents.md`](./agents.md))
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- **Circuit Breaker**: Halts all trading via `SystemExit` when daily P&L drops below -3.0%
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- Threshold may only be made stricter, never relaxed
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@@ -118,7 +123,79 @@ High-frequency trading with individual stock analysis:
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- **Fat-Finger Protection**: Rejects orders exceeding 30% of available cash
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- Must always be enforced, cannot be disabled
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### 5. Notifications (`src/notifications/telegram_client.py`)
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### 5. Strategy (`src/strategy/`)
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**Pre-Market Planner** (`pre_market_planner.py`) — AI playbook generation
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- Runs before market open (configurable `PRE_MARKET_MINUTES`, default 30)
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- Generates scenario-based playbooks via single Gemini API call per market
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- Handles timeout (`PLANNER_TIMEOUT_SECONDS`, default 60) with defensive playbook fallback
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- Persists playbooks to database for audit trail
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**Scenario Engine** (`scenario_engine.py`) — Local scenario matching
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- Matches live market data against pre-computed playbook scenarios
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- No AI calls during trading hours — pure Python matching logic
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- Returns matched scenarios with confidence scores
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- Configurable `MAX_SCENARIOS_PER_STOCK` (default 5)
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- Periodic rescan at `RESCAN_INTERVAL_SECONDS` (default 300)
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**Playbook Store** (`playbook_store.py`) — Playbook persistence
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- SQLite-backed storage for daily playbooks
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- Date and market-based retrieval
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- Status tracking (generated, active, expired)
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**Models** (`models.py`) — Pydantic data models
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- Scenario, Playbook, MatchResult, and related type definitions
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### 6. Context System (`src/context/`)
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**Context Store** (`store.py`) — L1-L7 hierarchical memory
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- 7-layer context system (see [docs/context-tree.md](./context-tree.md)):
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- L1: Tick-level (real-time price)
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- L2: Intraday (session summary)
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- L3: Daily (end-of-day)
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- L4: Weekly (trend analysis)
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- L5: Monthly (strategy review)
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- L6: Daily Review (scorecard)
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- L7: Evolution (long-term learning)
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- Key-value storage with timeframe tagging
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- SQLite persistence in `contexts` table
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**Context Scheduler** (`scheduler.py`) — Periodic aggregation
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- Scheduled summarization from lower to higher layers
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- Configurable aggregation intervals
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**Context Summarizer** (`summarizer.py`) — Layer summarization
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- Aggregates lower-layer data into higher-layer summaries
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### 7. Dashboard (`src/dashboard/`)
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**FastAPI App** (`app.py`) — Read-only monitoring dashboard
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- Runs as daemon thread when enabled (`--dashboard` CLI flag or `DASHBOARD_ENABLED=true`)
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- Configurable host/port (`DASHBOARD_HOST`, `DASHBOARD_PORT`, default `127.0.0.1:8080`)
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- Serves static HTML frontend
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**8 API Endpoints:**
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| Endpoint | Method | Description |
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|----------|--------|-------------|
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| `/` | GET | Static HTML dashboard |
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| `/api/status` | GET | Daily trading status by market |
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| `/api/playbook/{date}` | GET | Playbook for specific date and market |
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| `/api/scorecard/{date}` | GET | Daily scorecard from L6_DAILY context |
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| `/api/performance` | GET | Trading performance metrics (by market + combined) |
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| `/api/context/{layer}` | GET | Query context by layer (L1-L7) |
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| `/api/decisions` | GET | Decision log entries with outcomes |
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| `/api/scenarios/active` | GET | Today's matched scenarios |
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### 8. Notifications (`src/notifications/telegram_client.py`)
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**TelegramClient** — Real-time event notifications via Telegram Bot API
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@@ -126,7 +203,13 @@ High-frequency trading with individual stock analysis:
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- Non-blocking: failures are logged but never crash trading
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- Rate-limited: 1 message/second default to respect Telegram API limits
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- Auto-disabled when credentials missing
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- Gracefully handles API errors, network timeouts, invalid tokens
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**TelegramCommandHandler** — Bidirectional command interface
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- Long polling from Telegram API (configurable `TELEGRAM_POLLING_INTERVAL`)
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- 9 interactive commands: `/help`, `/status`, `/positions`, `/report`, `/scenarios`, `/review`, `/dashboard`, `/stop`, `/resume`
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- Authorization filtering by `TELEGRAM_CHAT_ID`
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- Enable/disable via `TELEGRAM_COMMANDS_ENABLED` (default: true)
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**Notification Types:**
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- Trade execution (BUY/SELL with confidence)
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@@ -134,12 +217,12 @@ High-frequency trading with individual stock analysis:
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- Fat-finger protection triggers (order rejection)
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- Market open/close events
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- System startup/shutdown status
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- Playbook generation results
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- Stop-loss monitoring alerts
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**Setup:** See [src/notifications/README.md](../src/notifications/README.md) for bot creation and configuration.
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### 9. Evolution (`src/evolution/`)
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### 6. Evolution (`src/evolution/optimizer.py`)
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**StrategyOptimizer** — Self-improvement loop
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**StrategyOptimizer** (`optimizer.py`) — Self-improvement loop
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- Analyzes high-confidence losing trades from SQLite
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- Asks Gemini to generate new `BaseStrategy` subclasses
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@@ -147,8 +230,122 @@ High-frequency trading with individual stock analysis:
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- Simulates PR creation for human review
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- Only activates strategies that pass all tests
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**DailyReview** (`daily_review.py`) — End-of-day review
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- Generates comprehensive trade performance summary
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- Stores results in L6_DAILY context layer
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- Tracks win rate, P&L, confidence accuracy
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**DailyScorecard** (`scorecard.py`) — Performance scoring
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- Calculates daily metrics (trades, P&L, win rate, avg confidence)
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- Enables trend tracking across days
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**Stop-Loss Monitoring** — Real-time position protection
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- Monitors positions against stop-loss levels from playbook scenarios
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- Sends Telegram alerts when thresholds approached or breached
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### 10. Decision Logger (`src/logging/decision_logger.py`)
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**DecisionLogger** — Comprehensive audit trail
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- Logs every trading decision with full context snapshot
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- Captures input data, rationale, confidence, and outcomes
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- Supports outcome tracking (P&L, accuracy) for post-analysis
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- Stored in `decision_logs` table with indexed queries
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- Review workflow support (reviewed flag, review notes)
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### 11. Data Integration (`src/data/`)
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**External Data Sources** (optional):
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- `news_api.py` — News sentiment data
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- `market_data.py` — Extended market data
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- `economic_calendar.py` — Economic event calendar
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### 12. Backup (`src/backup/`)
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**Disaster Recovery** (see [docs/disaster_recovery.md](./disaster_recovery.md)):
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- `scheduler.py` — Automated backup scheduling
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- `exporter.py` — Data export to various formats
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- `cloud_storage.py` — S3-compatible cloud backup
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- `health_monitor.py` — Backup integrity verification
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## Data Flow
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### Playbook Mode (Daily — Primary v2 Flow)
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```
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┌─────────────────────────────────────────────────────────────┐
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│ Pre-Market Phase (before market open) │
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└─────────────────────────────────────────────────────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ Pre-Market Planner │
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│ - 1 Gemini API call per market │
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│ - Generate scenario playbook │
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│ - Store in playbooks table │
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└──────────────────┬───────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────────┐
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│ Trading Hours (market open → close) │
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└─────────────────────────────────────────────────────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ Market Schedule Check │
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│ - Get open markets │
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│ - Filter by enabled markets │
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└──────────────────┬───────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ Scenario Engine (local) │
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│ - Match live data vs playbook │
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│ - No AI calls needed │
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│ - Return matched scenarios │
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└──────────────────┬───────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ Risk Manager: Validate Order │
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│ - Check circuit breaker │
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│ - Check fat-finger limit │
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└──────────────────┬───────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ Broker: Execute Order │
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│ - Domestic: send_order() │
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│ - Overseas: send_overseas_order()│
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└──────────────────┬───────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ Decision Logger + DB │
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│ - Full audit trail │
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│ - Context snapshot │
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│ - Telegram notification │
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└──────────────────┬───────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────────┐
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│ Post-Market Phase │
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└─────────────────────────────────────────────────────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ Daily Review + Scorecard │
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│ - Performance summary │
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│ - Store in L6_DAILY context │
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│ - Evolution learning │
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└──────────────────────────────────┘
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```
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### Realtime Mode (with Smart Scanner)
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||||
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```
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@@ -162,7 +359,7 @@ High-frequency trading with individual stock analysis:
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│ - Get open markets │
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│ - Filter by enabled markets │
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│ - Wait if all closed │
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└──────────────────┬────────────────┘
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└──────────────────┬───────────────┘
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│
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▼
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┌──────────────────────────────────┐
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@@ -172,25 +369,19 @@ High-frequency trading with individual stock analysis:
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│ - Calculate RSI(14) + vol ratio │
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│ - Filter: vol>2x AND RSI extreme │
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│ - Return top 3 qualified stocks │
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└──────────────────┬────────────────┘
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└──────────────────┬───────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ For Each Qualified Candidate │
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└──────────────────┬────────────────┘
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||||
└──────────────────┬───────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ Broker: Fetch Market Data │
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│ - Domestic: orderbook + balance │
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│ - Overseas: price + balance │
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└──────────────────┬────────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ Calculate P&L │
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│ pnl_pct = (eval - cost) / cost │
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└──────────────────┬────────────────┘
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└──────────────────┬───────────────┘
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│
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▼
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┌──────────────────────────────────┐
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@@ -199,47 +390,36 @@ High-frequency trading with individual stock analysis:
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│ - Call Gemini API │
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│ - Parse JSON response │
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│ - Return TradeDecision │
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└──────────────────┬────────────────┘
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└──────────────────┬───────────────┘
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│
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▼
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┌──────────────────────────────────┐
|
||||
│ Risk Manager: Validate Order │
|
||||
│ - Check circuit breaker │
|
||||
│ - Check fat-finger limit │
|
||||
│ - Raise if validation fails │
|
||||
└──────────────────┬────────────────┘
|
||||
└──────────────────┬───────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────────────┐
|
||||
│ Broker: Execute Order │
|
||||
│ - Domestic: send_order() │
|
||||
│ - Overseas: send_overseas_order()│
|
||||
└──────────────────┬────────────────┘
|
||||
└──────────────────┬───────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────────────┐
|
||||
│ Notifications: Send Alert │
|
||||
│ - Trade execution notification │
|
||||
│ - Non-blocking (errors logged) │
|
||||
│ - Rate-limited to 1/sec │
|
||||
└──────────────────┬────────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────────────┐
|
||||
│ Database: Log Trade │
|
||||
│ - SQLite (data/trades.db) │
|
||||
│ - Track: action, confidence, │
|
||||
│ rationale, market, exchange │
|
||||
│ - NEW: selection_context (JSON) │
|
||||
│ - RSI, volume_ratio, signal │
|
||||
│ - For Evolution optimization │
|
||||
└───────────────────────────────────┘
|
||||
│ Decision Logger + Notifications │
|
||||
│ - Log trade to SQLite │
|
||||
│ - selection_context (JSON) │
|
||||
│ - Telegram notification │
|
||||
└──────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Database Schema
|
||||
|
||||
**SQLite** (`src/db.py`)
|
||||
**SQLite** (`src/db.py`) — Database: `data/trades.db`
|
||||
|
||||
### trades
|
||||
```sql
|
||||
CREATE TABLE trades (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
@@ -251,25 +431,73 @@ CREATE TABLE trades (
|
||||
quantity INTEGER,
|
||||
price REAL,
|
||||
pnl REAL DEFAULT 0.0,
|
||||
market TEXT DEFAULT 'KR', -- KR | US_NASDAQ | JP | etc.
|
||||
exchange_code TEXT DEFAULT 'KRX', -- KRX | NASD | NYSE | etc.
|
||||
selection_context TEXT -- JSON: {rsi, volume_ratio, signal, score}
|
||||
market TEXT DEFAULT 'KR',
|
||||
exchange_code TEXT DEFAULT 'KRX',
|
||||
selection_context TEXT, -- JSON: {rsi, volume_ratio, signal, score}
|
||||
decision_id TEXT -- Links to decision_logs
|
||||
);
|
||||
```
|
||||
|
||||
**Selection Context** (new in v0.9.0): Stores scanner selection criteria as JSON:
|
||||
```json
|
||||
{
|
||||
"rsi": 28.5,
|
||||
"volume_ratio": 2.7,
|
||||
"signal": "oversold",
|
||||
"score": 85.2
|
||||
}
|
||||
### contexts
|
||||
```sql
|
||||
CREATE TABLE contexts (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
layer TEXT NOT NULL, -- L1 through L7
|
||||
timeframe TEXT,
|
||||
key TEXT NOT NULL,
|
||||
value TEXT NOT NULL, -- JSON data
|
||||
created_at TEXT NOT NULL,
|
||||
updated_at TEXT NOT NULL
|
||||
);
|
||||
-- Indices: idx_contexts_layer, idx_contexts_timeframe, idx_contexts_updated
|
||||
```
|
||||
|
||||
Enables Evolution system to analyze correlation between selection criteria and trade outcomes.
|
||||
### decision_logs
|
||||
```sql
|
||||
CREATE TABLE decision_logs (
|
||||
decision_id TEXT PRIMARY KEY,
|
||||
timestamp TEXT NOT NULL,
|
||||
stock_code TEXT,
|
||||
market TEXT,
|
||||
exchange_code TEXT,
|
||||
action TEXT,
|
||||
confidence INTEGER,
|
||||
rationale TEXT,
|
||||
context_snapshot TEXT, -- JSON: full context at decision time
|
||||
input_data TEXT, -- JSON: market data used
|
||||
outcome_pnl REAL,
|
||||
outcome_accuracy REAL,
|
||||
reviewed INTEGER DEFAULT 0,
|
||||
review_notes TEXT
|
||||
);
|
||||
-- Indices: idx_decision_logs_timestamp, idx_decision_logs_reviewed, idx_decision_logs_confidence
|
||||
```
|
||||
|
||||
Auto-migration: Adds `market`, `exchange_code`, and `selection_context` columns if missing for backward compatibility.
|
||||
### playbooks
|
||||
```sql
|
||||
CREATE TABLE playbooks (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
date TEXT NOT NULL,
|
||||
market TEXT NOT NULL,
|
||||
status TEXT DEFAULT 'generated',
|
||||
playbook_json TEXT NOT NULL, -- Full playbook with scenarios
|
||||
generated_at TEXT NOT NULL,
|
||||
token_count INTEGER,
|
||||
scenario_count INTEGER,
|
||||
match_count INTEGER DEFAULT 0
|
||||
);
|
||||
-- Indices: idx_playbooks_date, idx_playbooks_market
|
||||
```
|
||||
|
||||
### context_metadata
|
||||
```sql
|
||||
CREATE TABLE context_metadata (
|
||||
layer TEXT PRIMARY KEY,
|
||||
description TEXT,
|
||||
retention_days INTEGER,
|
||||
aggregation_source TEXT
|
||||
);
|
||||
```
|
||||
|
||||
## Configuration
|
||||
|
||||
@@ -284,29 +512,62 @@ KIS_APP_SECRET=your_app_secret
|
||||
KIS_ACCOUNT_NO=XXXXXXXX-XX
|
||||
GEMINI_API_KEY=your_gemini_key
|
||||
|
||||
# Optional
|
||||
# Optional — Trading Mode
|
||||
MODE=paper # paper | live
|
||||
DB_PATH=data/trades.db
|
||||
CONFIDENCE_THRESHOLD=80
|
||||
MAX_LOSS_PCT=3.0
|
||||
MAX_ORDER_PCT=30.0
|
||||
ENABLED_MARKETS=KR,US_NASDAQ # Comma-separated market codes
|
||||
|
||||
# Trading Mode (API efficiency)
|
||||
TRADE_MODE=daily # daily | realtime
|
||||
DAILY_SESSIONS=4 # Sessions per day (daily mode only)
|
||||
SESSION_INTERVAL_HOURS=6 # Hours between sessions (daily mode only)
|
||||
|
||||
# Telegram Notifications (optional)
|
||||
TELEGRAM_BOT_TOKEN=1234567890:ABCdefGHIjklMNOpqrsTUVwxyz
|
||||
TELEGRAM_CHAT_ID=123456789
|
||||
TELEGRAM_ENABLED=true
|
||||
# Optional — Database
|
||||
DB_PATH=data/trades.db
|
||||
|
||||
# Smart Scanner (optional, realtime mode only)
|
||||
# Optional — Risk
|
||||
CONFIDENCE_THRESHOLD=80
|
||||
MAX_LOSS_PCT=3.0
|
||||
MAX_ORDER_PCT=30.0
|
||||
|
||||
# Optional — Markets
|
||||
ENABLED_MARKETS=KR,US # Comma-separated market codes
|
||||
RATE_LIMIT_RPS=2.0 # KIS API requests per second
|
||||
|
||||
# Optional — Pre-Market Planner (v2)
|
||||
PRE_MARKET_MINUTES=30 # Minutes before market open to generate playbook
|
||||
MAX_SCENARIOS_PER_STOCK=5 # Max scenarios per stock in playbook
|
||||
PLANNER_TIMEOUT_SECONDS=60 # Timeout for playbook generation
|
||||
DEFENSIVE_PLAYBOOK_ON_FAILURE=true # Fallback on AI failure
|
||||
RESCAN_INTERVAL_SECONDS=300 # Scenario rescan interval during trading
|
||||
|
||||
# Optional — Smart Scanner (realtime mode only)
|
||||
RSI_OVERSOLD_THRESHOLD=30 # 0-50, oversold threshold
|
||||
RSI_MOMENTUM_THRESHOLD=70 # 50-100, momentum threshold
|
||||
VOL_MULTIPLIER=2.0 # Minimum volume ratio (2.0 = 200%)
|
||||
SCANNER_TOP_N=3 # Max qualified candidates per scan
|
||||
|
||||
# Optional — Dashboard
|
||||
DASHBOARD_ENABLED=false # Enable FastAPI dashboard
|
||||
DASHBOARD_HOST=127.0.0.1 # Dashboard bind address
|
||||
DASHBOARD_PORT=8080 # Dashboard port (1-65535)
|
||||
|
||||
# Optional — Telegram
|
||||
TELEGRAM_BOT_TOKEN=1234567890:ABCdefGHIjklMNOpqrsTUVwxyz
|
||||
TELEGRAM_CHAT_ID=123456789
|
||||
TELEGRAM_ENABLED=true
|
||||
TELEGRAM_COMMANDS_ENABLED=true # Enable bidirectional commands
|
||||
TELEGRAM_POLLING_INTERVAL=1.0 # Command polling interval (seconds)
|
||||
|
||||
# Optional — Backup
|
||||
BACKUP_ENABLED=false
|
||||
BACKUP_DIR=data/backups
|
||||
S3_ENDPOINT_URL=...
|
||||
S3_ACCESS_KEY=...
|
||||
S3_SECRET_KEY=...
|
||||
S3_BUCKET_NAME=...
|
||||
S3_REGION=...
|
||||
|
||||
# Optional — External Data
|
||||
NEWS_API_KEY=...
|
||||
NEWS_API_PROVIDER=...
|
||||
MARKET_DATA_API_KEY=...
|
||||
```
|
||||
|
||||
Tests use in-memory SQLite (`DB_PATH=":memory:"`) and dummy credentials via `tests/conftest.py`.
|
||||
@@ -340,4 +601,9 @@ Tests use in-memory SQLite (`DB_PATH=":memory:"`) and dummy credentials via `tes
|
||||
- Invalid token → log error, trading unaffected
|
||||
- Rate limit exceeded → queued via rate limiter
|
||||
|
||||
**Guarantee**: Notification failures never interrupt trading operations.
|
||||
### Playbook Generation Failure
|
||||
- Timeout → fall back to defensive playbook (`DEFENSIVE_PLAYBOOK_ON_FAILURE`)
|
||||
- API error → use previous day's playbook if available
|
||||
- No playbook → skip pre-market phase, fall back to direct AI calls
|
||||
|
||||
**Guarantee**: Notification and dashboard failures never interrupt trading operations.
|
||||
|
||||
@@ -119,7 +119,7 @@ No decorator needed for async tests.
|
||||
# Install all dependencies (production + dev)
|
||||
pip install -e ".[dev]"
|
||||
|
||||
# Run full test suite with coverage
|
||||
# Run full test suite with coverage (551 tests across 25 files)
|
||||
pytest -v --cov=src --cov-report=term-missing
|
||||
|
||||
# Run a single test file
|
||||
@@ -137,11 +137,61 @@ mypy src/ --strict
|
||||
# Run the trading agent
|
||||
python -m src.main --mode=paper
|
||||
|
||||
# Run with dashboard enabled
|
||||
python -m src.main --mode=paper --dashboard
|
||||
|
||||
# Docker
|
||||
docker compose up -d ouroboros # Run agent
|
||||
docker compose --profile test up test # Run tests in container
|
||||
```
|
||||
|
||||
## Dashboard
|
||||
|
||||
The FastAPI dashboard provides read-only monitoring of the trading system.
|
||||
|
||||
### Starting the Dashboard
|
||||
|
||||
```bash
|
||||
# Via CLI flag
|
||||
python -m src.main --mode=paper --dashboard
|
||||
|
||||
# Via environment variable
|
||||
DASHBOARD_ENABLED=true python -m src.main --mode=paper
|
||||
```
|
||||
|
||||
Dashboard runs as a daemon thread on `DASHBOARD_HOST:DASHBOARD_PORT` (default: `127.0.0.1:8080`).
|
||||
|
||||
### API Endpoints
|
||||
|
||||
| Endpoint | Description |
|
||||
|----------|-------------|
|
||||
| `GET /` | HTML dashboard UI |
|
||||
| `GET /api/status` | Daily trading status by market |
|
||||
| `GET /api/playbook/{date}` | Playbook for specific date (query: `market`) |
|
||||
| `GET /api/scorecard/{date}` | Daily scorecard from L6_DAILY context |
|
||||
| `GET /api/performance` | Performance metrics by market and combined |
|
||||
| `GET /api/context/{layer}` | Context data by layer L1-L7 (query: `timeframe`) |
|
||||
| `GET /api/decisions` | Decision log entries (query: `limit`, `market`) |
|
||||
| `GET /api/scenarios/active` | Today's matched scenarios |
|
||||
|
||||
## Telegram Commands
|
||||
|
||||
When `TELEGRAM_COMMANDS_ENABLED=true` (default), the bot accepts these interactive commands:
|
||||
|
||||
| Command | Description |
|
||||
|---------|-------------|
|
||||
| `/help` | List available commands |
|
||||
| `/status` | Show trading status (mode, markets, P&L) |
|
||||
| `/positions` | Display account summary (balance, cash, P&L) |
|
||||
| `/report` | Daily summary metrics (trades, P&L, win rate) |
|
||||
| `/scenarios` | Show today's playbook scenarios |
|
||||
| `/review` | Display recent scorecards (L6_DAILY layer) |
|
||||
| `/dashboard` | Show dashboard URL if enabled |
|
||||
| `/stop` | Pause trading |
|
||||
| `/resume` | Resume trading |
|
||||
|
||||
Commands are only processed from the authorized `TELEGRAM_CHAT_ID`.
|
||||
|
||||
## Environment Setup
|
||||
|
||||
```bash
|
||||
|
||||
@@ -86,3 +86,28 @@
|
||||
- Plan Consistency (필수), Safety & Constraints, Quality, Workflow 4개 카테고리
|
||||
|
||||
**이슈/PR:** #114
|
||||
|
||||
---
|
||||
|
||||
## 2026-02-16
|
||||
|
||||
### 문서 v2 동기화 (전체 문서 현행화)
|
||||
|
||||
**배경:**
|
||||
- v2 기능 구현 완료 후 문서가 실제 코드 상태와 크게 괴리
|
||||
- 문서에는 54 tests / 4 files로 기록되었으나 실제로는 551 tests / 25 files
|
||||
- v2 핵심 기능(Playbook, Scenario Engine, Dashboard, Telegram Commands, Daily Review, Context System, Backup) 문서화 누락
|
||||
|
||||
**요구사항:**
|
||||
1. `docs/testing.md` — 551 tests / 25 files 반영, 전체 테스트 파일 설명
|
||||
2. `docs/architecture.md` — v2 컴포넌트(Strategy, Context, Dashboard, Decision Logger 등) 추가, Playbook Mode 데이터 플로우, DB 스키마 5개 테이블, v2 환경변수
|
||||
3. `docs/commands.md` — Dashboard 실행 명령어, Telegram 명령어 9종 레퍼런스
|
||||
4. `CLAUDE.md` — Project Structure 트리 확장, 테스트 수 업데이트, `--dashboard` 플래그
|
||||
5. `docs/skills.md` — DB 파일명 `trades.db`로 통일, Dashboard 명령어 추가
|
||||
6. 기존에 유효한 트러블슈팅, 코드 예제 등은 유지
|
||||
|
||||
**구현 결과:**
|
||||
- 6개 문서 파일 업데이트
|
||||
- 이전 시도(2개 커밋)는 기존 내용을 과도하게 삭제하여 폐기, main 기준으로 재작업
|
||||
|
||||
**이슈/PR:** #131, PR #134
|
||||
|
||||
@@ -34,6 +34,12 @@ python -m src.main --mode=paper
|
||||
```
|
||||
Runs the agent in paper-trading mode (no real orders).
|
||||
|
||||
### Start Trading Agent with Dashboard
|
||||
```bash
|
||||
python -m src.main --mode=paper --dashboard
|
||||
```
|
||||
Runs the agent with FastAPI dashboard on `127.0.0.1:8080` (configurable via `DASHBOARD_HOST`/`DASHBOARD_PORT`).
|
||||
|
||||
### Start Trading Agent (Production)
|
||||
```bash
|
||||
docker compose up -d ouroboros
|
||||
@@ -59,7 +65,7 @@ Analyze the last 30 days of trade logs and generate performance metrics.
|
||||
python -m src.evolution.optimizer --evolve
|
||||
```
|
||||
Triggers the evolution engine to:
|
||||
1. Analyze `trade_logs.db` for failing patterns
|
||||
1. Analyze `trades.db` for failing patterns
|
||||
2. Ask Gemini to generate a new strategy
|
||||
3. Run tests on the new strategy
|
||||
4. Create a PR if tests pass
|
||||
@@ -91,12 +97,12 @@ curl http://localhost:8080/health
|
||||
|
||||
### View Trade Logs
|
||||
```bash
|
||||
sqlite3 data/trade_logs.db "SELECT * FROM trades ORDER BY timestamp DESC LIMIT 20;"
|
||||
sqlite3 data/trades.db "SELECT * FROM trades ORDER BY timestamp DESC LIMIT 20;"
|
||||
```
|
||||
|
||||
### Export Trade History
|
||||
```bash
|
||||
sqlite3 -header -csv data/trade_logs.db "SELECT * FROM trades;" > trades_export.csv
|
||||
sqlite3 -header -csv data/trades.db "SELECT * FROM trades;" > trades_export.csv
|
||||
```
|
||||
|
||||
## Safety Checklist (Pre-Deploy)
|
||||
|
||||
206
docs/testing.md
206
docs/testing.md
@@ -2,51 +2,29 @@
|
||||
|
||||
## Test Structure
|
||||
|
||||
**54 tests** across four files. `asyncio_mode = "auto"` in pyproject.toml — async tests need no special decorator.
|
||||
**551 tests** across **25 files**. `asyncio_mode = "auto"` in pyproject.toml — async tests need no special decorator.
|
||||
|
||||
The `settings` fixture in `conftest.py` provides safe defaults with test credentials and in-memory DB.
|
||||
|
||||
### Test Files
|
||||
|
||||
#### `tests/test_risk.py` (11 tests)
|
||||
- Circuit breaker boundaries
|
||||
- Fat-finger edge cases
|
||||
#### Core Components
|
||||
|
||||
##### `tests/test_risk.py` (14 tests)
|
||||
- Circuit breaker boundaries and exact threshold triggers
|
||||
- Fat-finger edge cases and percentage validation
|
||||
- P&L calculation edge cases
|
||||
- Order validation logic
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
def test_circuit_breaker_exact_threshold(risk_manager):
|
||||
"""Circuit breaker should trip at exactly -3.0%."""
|
||||
with pytest.raises(CircuitBreakerTripped):
|
||||
risk_manager.validate_order(
|
||||
current_pnl_pct=-3.0,
|
||||
order_amount=1000,
|
||||
total_cash=10000
|
||||
)
|
||||
```
|
||||
|
||||
#### `tests/test_broker.py` (6 tests)
|
||||
##### `tests/test_broker.py` (11 tests)
|
||||
- OAuth token lifecycle
|
||||
- Rate limiting enforcement
|
||||
- Hash key generation
|
||||
- Network error handling
|
||||
- SSL context configuration
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
async def test_rate_limiter(broker):
|
||||
"""Rate limiter should delay requests to stay under 10 RPS."""
|
||||
start = time.monotonic()
|
||||
for _ in range(15): # 15 requests
|
||||
await broker._rate_limiter.acquire()
|
||||
elapsed = time.monotonic() - start
|
||||
assert elapsed >= 1.0 # Should take at least 1 second
|
||||
```
|
||||
|
||||
#### `tests/test_brain.py` (18 tests)
|
||||
- Valid JSON parsing
|
||||
- Markdown-wrapped JSON handling
|
||||
##### `tests/test_brain.py` (24 tests)
|
||||
- Valid JSON parsing and markdown-wrapped JSON handling
|
||||
- Malformed JSON fallback
|
||||
- Missing fields handling
|
||||
- Invalid action validation
|
||||
@@ -54,33 +32,143 @@ async def test_rate_limiter(broker):
|
||||
- Empty response handling
|
||||
- Prompt construction for different markets
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
async def test_confidence_below_threshold_forces_hold(brain):
|
||||
"""Decisions below confidence threshold should force HOLD."""
|
||||
decision = brain.parse_response('{"action":"BUY","confidence":70,"rationale":"test"}')
|
||||
assert decision.action == "HOLD"
|
||||
assert decision.confidence == 70
|
||||
```
|
||||
|
||||
#### `tests/test_market_schedule.py` (19 tests)
|
||||
##### `tests/test_market_schedule.py` (24 tests)
|
||||
- Market open/close logic
|
||||
- Timezone handling (UTC, Asia/Seoul, America/New_York, etc.)
|
||||
- DST (Daylight Saving Time) transitions
|
||||
- Weekend handling
|
||||
- Lunch break logic
|
||||
- Weekend handling and lunch break logic
|
||||
- Multiple market filtering
|
||||
- Next market open calculation
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
def test_is_market_open_during_trading_hours():
|
||||
"""Market should be open during regular trading hours."""
|
||||
# KRX: 9:00-15:30 KST, no lunch break
|
||||
market = MARKETS["KR"]
|
||||
trading_time = datetime(2026, 2, 3, 10, 0, tzinfo=ZoneInfo("Asia/Seoul")) # Monday 10:00
|
||||
assert is_market_open(market, trading_time) is True
|
||||
```
|
||||
##### `tests/test_db.py` (3 tests)
|
||||
- Database initialization and table creation
|
||||
- Trade logging with all fields (market, exchange_code, decision_id)
|
||||
- Query and retrieval operations
|
||||
|
||||
##### `tests/test_main.py` (37 tests)
|
||||
- Trading loop orchestration
|
||||
- Market iteration and stock processing
|
||||
- Dashboard integration (`--dashboard` flag)
|
||||
- Telegram command handler wiring
|
||||
- Error handling and graceful shutdown
|
||||
|
||||
#### Strategy & Playbook (v2)
|
||||
|
||||
##### `tests/test_pre_market_planner.py` (37 tests)
|
||||
- Pre-market playbook generation
|
||||
- Gemini API integration for scenario creation
|
||||
- Timeout handling and defensive playbook fallback
|
||||
- Multi-market playbook generation
|
||||
|
||||
##### `tests/test_scenario_engine.py` (44 tests)
|
||||
- Scenario matching against live market data
|
||||
- Confidence scoring and threshold filtering
|
||||
- Multiple scenario type handling
|
||||
- Edge cases (no match, partial match, expired scenarios)
|
||||
|
||||
##### `tests/test_playbook_store.py` (23 tests)
|
||||
- Playbook persistence to SQLite
|
||||
- Date-based retrieval and market filtering
|
||||
- Playbook status management (generated, active, expired)
|
||||
- JSON serialization/deserialization
|
||||
|
||||
##### `tests/test_strategy_models.py` (33 tests)
|
||||
- Pydantic model validation for scenarios, playbooks, decisions
|
||||
- Field constraints and default values
|
||||
- Serialization round-trips
|
||||
|
||||
#### Analysis & Scanning
|
||||
|
||||
##### `tests/test_volatility.py` (24 tests)
|
||||
- ATR and RSI calculation accuracy
|
||||
- Volume surge ratio computation
|
||||
- Momentum scoring
|
||||
- Breakout/breakdown pattern detection
|
||||
- Market scanner watchlist management
|
||||
|
||||
##### `tests/test_smart_scanner.py` (13 tests)
|
||||
- Python-first filtering pipeline
|
||||
- RSI and volume ratio filter logic
|
||||
- Candidate scoring and ranking
|
||||
- Fallback to static watchlist
|
||||
|
||||
#### Context & Memory
|
||||
|
||||
##### `tests/test_context.py` (18 tests)
|
||||
- L1-L7 layer storage and retrieval
|
||||
- Context key-value CRUD operations
|
||||
- Timeframe-based queries
|
||||
- Layer metadata management
|
||||
|
||||
##### `tests/test_context_scheduler.py` (5 tests)
|
||||
- Periodic context aggregation scheduling
|
||||
- Layer summarization triggers
|
||||
|
||||
#### Evolution & Review
|
||||
|
||||
##### `tests/test_evolution.py` (24 tests)
|
||||
- Strategy optimization loop
|
||||
- High-confidence losing trade analysis
|
||||
- Generated strategy validation
|
||||
|
||||
##### `tests/test_daily_review.py` (10 tests)
|
||||
- End-of-day review generation
|
||||
- Trade performance summarization
|
||||
- Context layer (L6_DAILY) integration
|
||||
|
||||
##### `tests/test_scorecard.py` (3 tests)
|
||||
- Daily scorecard metrics calculation
|
||||
- Win rate, P&L, confidence tracking
|
||||
|
||||
#### Notifications & Commands
|
||||
|
||||
##### `tests/test_telegram.py` (25 tests)
|
||||
- Message sending and formatting
|
||||
- Rate limiting (leaky bucket)
|
||||
- Error handling (network timeout, invalid token)
|
||||
- Auto-disable on missing credentials
|
||||
- Notification types (trade, circuit breaker, fat-finger, market events)
|
||||
|
||||
##### `tests/test_telegram_commands.py` (31 tests)
|
||||
- 9 command handlers (/help, /status, /positions, /report, /scenarios, /review, /dashboard, /stop, /resume)
|
||||
- Long polling and command dispatch
|
||||
- Authorization filtering by chat_id
|
||||
- Command response formatting
|
||||
|
||||
#### Dashboard
|
||||
|
||||
##### `tests/test_dashboard.py` (14 tests)
|
||||
- FastAPI endpoint responses (8 API routes)
|
||||
- Status, playbook, scorecard, performance, context, decisions, scenarios
|
||||
- Query parameter handling (market, date, limit)
|
||||
|
||||
#### Performance & Quality
|
||||
|
||||
##### `tests/test_token_efficiency.py` (34 tests)
|
||||
- Gemini token usage optimization
|
||||
- Prompt size reduction verification
|
||||
- Cache effectiveness
|
||||
|
||||
##### `tests/test_latency_control.py` (30 tests)
|
||||
- API call latency measurement
|
||||
- Rate limiter timing accuracy
|
||||
- Async operation overhead
|
||||
|
||||
##### `tests/test_decision_logger.py` (9 tests)
|
||||
- Decision audit trail completeness
|
||||
- Context snapshot capture
|
||||
- Outcome tracking (P&L, accuracy)
|
||||
|
||||
##### `tests/test_data_integration.py` (38 tests)
|
||||
- External data source integration
|
||||
- News API, market data, economic calendar
|
||||
- Error handling for API failures
|
||||
|
||||
##### `tests/test_backup.py` (23 tests)
|
||||
- Backup scheduler and execution
|
||||
- Cloud storage (S3) upload
|
||||
- Health monitoring
|
||||
- Data export functionality
|
||||
|
||||
## Coverage Requirements
|
||||
|
||||
@@ -91,20 +179,6 @@ Check coverage:
|
||||
pytest -v --cov=src --cov-report=term-missing
|
||||
```
|
||||
|
||||
Expected output:
|
||||
```
|
||||
Name Stmts Miss Cover Missing
|
||||
-----------------------------------------------------------
|
||||
src/brain/gemini_client.py 85 5 94% 165-169
|
||||
src/broker/kis_api.py 120 12 90% ...
|
||||
src/core/risk_manager.py 35 2 94% ...
|
||||
src/db.py 25 1 96% ...
|
||||
src/main.py 150 80 47% (excluded from CI)
|
||||
src/markets/schedule.py 95 3 97% ...
|
||||
-----------------------------------------------------------
|
||||
TOTAL 510 103 80%
|
||||
```
|
||||
|
||||
**Note:** `main.py` has lower coverage as it contains the main loop which is tested via integration/manual testing.
|
||||
|
||||
## Test Configuration
|
||||
|
||||
Reference in New Issue
Block a user